Allow augment to be used on newdata for mlogit

R/mlogit-tidiers.R

@@ -25,6 +25,11 @@
 #' augment(m)
 #' glance(m)
 #'
+#' # augment with newdata
+#' Fish2 <- Fish
+#' Fish2$price <- ifelse(Fish2$income < 3000, Fish2$price * 0.7, Fish2$price )
+#' augment(m, newdata = Fish2)
+#'
 #' @aliases mlogit_tidiers
 #' @export
 #' @family mlogit tidiers
@@ -56,23 +61,37 @@ tidy.mlogit <- function(x, conf.int = FALSE, conf.level = 0.95, ...) {
 #'
 #' @inherit tidy.mlogit params examples
 #' @param data Not currently used
+#' @param newdata Data frame on which to predict utility values. See `details`.
 #'
-#' @details At the moment this only works on the estimation dataset. Need to set
-#'   it up to predict on another dataset.
+#' @details Augmenting a new data frame requires that the data be
+#'   a `dfidx` data frame with ID and alternative information identified.
 #'
 #' @export
 #' @seealso [augment()]
 #' @family mlogit tidiers
 #'
 #'
-augment.mlogit <- function(x, data = x$model, ...) {
+augment.mlogit <- function(x, data = x$model, newdata = NULL, ...) {
   check_ellipses("newdata", "augment", "mlogit", ...)
+
+  # So, the way mlogit handles prediction is kind of silly, because
+  # the developers have chosen to not implement a model.matrix method.
+  # Rather, mlogit uses update to create a NEW model object but without 
+  # running a new maximum likelihood estimation, it constrains the parameters
+  # to their previously estimated values.
+  # This does unfortunately mean that the data to be predicted has to be
+  # in a dfidx format.
+  if (!is.null(newdata)) {
+    x <- update(x, start = coef(x, fixed = TRUE), data = newdata, iterlim = 0,
+                print.level = 0)
+  }
 
   # the ID variables are really messed up, so we're going to do some
   # retrofitting because this ends up being a pretty important element of
   # what we want to do with the results.
   idx <- x$model$idx
 
+  # augment 
   reg <- x$model %>%
     as_augment_tibble() %>%
     dplyr::select(-idx) %>%
@@ -85,11 +104,15 @@ augment.mlogit <- function(x, data = x$model, ...) {
     # reappend the id columns
     dplyr::mutate(
       id = idx$id1,
-      alternative = idx$id2,
-      .resid = as.vector(x$residuals)
+      alternative = idx$id2
     ) %>%
     dplyr::select(id, alternative, chosen, everything())
-
+
+  # residuals don't make sense for newdata
+  if(is.null(newdata)){
+    reg$.resid = as.vector(x$residuals)
+  }
+
   reg
 }